Air monitoring by nanopore sequencing

Abstract While the air microbiome and its diversity are essential for human health and ecosystem resilience, comprehensive air microbial diversity monitoring has remained rare, so that little is known about the air microbiome’s composition, distribution, or functionality. Here we show that nanopore sequencing-based metagenomics can robustly assess the air microbiome in combination with active air sampling through liquid impingement and tailored computational analysis. We provide fast and portable laboratory and computational approaches for air microbiome profiling, which we leverage to robustly assess the taxonomic composition of the core air microbiome of a controlled greenhouse environment and of a natural outdoor environment. We show that long-read sequencing can resolve species-level annotations and specific ecosystem functions through de novo metagenomic assemblies despite the low amount of fragmented DNA used as an input for nanopore sequencing. We then apply our pipeline to assess the diversity and variability of an urban air microbiome, using Barcelona, Spain, as an example; this randomized experiment gives first insights into the presence of highly stable location-specific air microbiomes within the city’s boundaries, and showcases the robust microbial assessments that can be achieved through automatable, fast, and portable nanopore sequencing technology.


Air sampling and DNA extraction optimizations
We first tested two standard air sampling approaches, the highvolume sampler (HVS, MCV, Spain) and the Coriolis µ liquid impinger (Bertin Technologies, France), to assess the optimal air sampler for their compatibility with nanopore shotgun sequencing.
For the HVS, we used quartz filters for air sampling for 24h at a rate of 500 L/min.We applied both, phenol-chloroform extraction [1] and the standard PowerSoil Pro kit (QIAGEN, 2018), to the filters.While the phenol-chloroform method resulted in a higher total DNA yield than the standard extraction kit (data not shown), the nanodrop nucleic acid 260/280 measurements of around 1.2 indicated that the extracted DNA was highly contaminated, most likely due to residual phenol, which would block the nanopores during shotgun sequencing.
The DNA yield of the standard extraction kit, on the other hand, was not sufficient for nanopore shotgun sequencing, which made us hypothesize that the standard kitsince not optimized for DNA extractions from quartz filtermight have chemically enhanced binding of the particles to the silica-enriched filters, and might therefore have made extraction inefficient.
For the liquid impingement-based and therefore filter-free sampler Coriolis µ, we sampled air for 1h at a rate of 300 L/min, and extracted sufficient DNA using the standard Qiagen kit: To increase DNA concentrations, we benchmarked that the volume of the final elution buffer (EB) could be reduced from the standard of 50 μL to 30μL.We further tested if a repeated washing of the spin column would further increase the DNA yield, which was not the case:

Functional annotation
The general functional analysis of the de novo assemblies and MAGs revealed a broad spectrum of COG (Clusters of Orthologous Genes) functional categories in our controlled and natural air samples.Briefly, gene predictions were made using Prodigal v2.6.3 [2], with COG functional categories analyzed using eggNOG v2.0.1 [3] and taxonomically classified using DIAMOND BLASTP.As eggNOG lacked taxonomic resolution, we also applied Prokka v1.14.6 [4] followed by DIAMOND BLASTP to the bins, which delivered taxonomic and functional annotation.Following the findings in 'Omics Insights in Environmental Bioremediation', we filtered the annotated gene list to select genes involved in biodegradation and bioremediation.For comparing the functional inferences between the different sampling durations and locations, we calculated the relative abundance of the functional categories for the contigs (Supplementary Figure 5A) and MAGs (Supplementary Figure 5B) across samples of each experiment.We found a broad spectrum of genes encompassing diverse COG functional categories.The gene distribution was relatively similar between the controlled and the environmental setting, which was expected given the very basic metabolic and replication functionalities that are being described by COG.
The functional annotation of MAGs further allowed us to predict taxon-specific functions of the air microbiome.We, for example, obtained a de novo assembly of Sphingomonas alba, which has previously only been defined through a soil isolate [5] and might therefore represent a novel strain with important functional variation.Our genome annotation identified genes (flr, ribBA) from flavin-based metabolic cycles, and a gene (cher1) which plays a role in biofilm formation and chemotaxis [6].Certain bacterial taxa exhibit chemotactic responses towards aromatic hydrocarbons, which are prevalent pollutants, since they utilize these compounds as carbon sources; the cher1 gene has been identified as a key gene in mediating this behavior [7].